Matching Items (25)
Filtering by
- All Subjects: engineering
- Genre: Academic theses
- Genre: Masters Thesis
- Creators: Redkar, Sangram
- Creators: Mignolet, Marc
- Member of: Theses and Dissertations
Description
The objective of this thesis was to compare various approaches for classification of the `good' and `bad' parts via non-destructive resonance testing methods by collecting and analyzing experimental data in the frequency and time domains. A Laser Scanning Vibrometer was employed to measure vibrations samples in order to determine the spectral characteristics such as natural frequencies and amplitudes. Statistical pattern recognition tools such as Hilbert Huang, Fisher's Discriminant, and Neural Network were used to identify and classify the unknown samples whether they are defective or not. In this work, a Finite Element Analysis software packages (ANSYS 13.0 and NASTRAN NX8.0) was used to obtain estimates of resonance frequencies in `good' and `bad' samples. Furthermore, a system identification approach was used to generate Auto-Regressive-Moving Average with exogenous component, Box-Jenkins, and Output Error models from experimental data that can be used for classification
ContributorsJameel, Osama (Author) / Redkar, Sangram (Thesis advisor) / Arizona State University (Publisher)
Created2013
Description
The objective of this work is to develop a Stop-Rotor Multimode UAV. This UAV is capable of vertical take-off and landing like a helicopter and can convert from a helicopter mode to an airplane mode in mid-flight. Thus, this UAV can hover as a helicopter and achieve high mission range of an airplane. The stop-rotor concept implies that in mid-flight the lift generating helicopter rotor stops and rotates the blades into airplane wings. The thrust in airplane mode is then provided by a pusher propeller. The aircraft configuration presents unique challenges in flight dynamics, modeling and control. In this thesis a mathematical model along with the design and simulations of a hover control will be presented. In addition, the discussion of the performance in fixed-wing flight, and the autopilot architecture of the UAV will be presented. Also presented, are some experimental "conversion" results where the Stop-Rotor aircraft was dropped from a hot air balloon and performed a successful conversion from helicopter to airplane mode.
ContributorsVargas-Clara, Alvaro (Author) / Redkar, Sangram (Thesis advisor) / Macia, Narciso (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
High Pressure Superheater 1 (HPSH1) is the first heat exchange tube bank inside the Heat Recovery Steam Generator (HRSG) to encounter exhaust flue gas from the gas turbine of a Combined Cycle Power Plant. Steam flowing through the HPSH1 gains heat from the flue gas prior to entering the steam turbine. During cold start-ups, rapid temperature changes in operating condition give rise to significant temperature gradients in the thick-walled components of HPSH1 (manifolds, links, and headers). These temperature gradients produce thermal-structural stresses in the components. The resulting high cycle fatigue is a major concern as this can lead to premature failure of the components. The main objective of this project was to address the thermal-structural stress field induced in HPSH1 during a typical cold start-up transient. To this end, computational fluid dynamics (CFD) was used to carry out the thermal-fluid analysis of HPSH1. The calculated temperature distributions in the component walls were the primary inputs for the finite element (FEA) model that performed structural analysis. Thermal-structural analysis was initially carried out at full-load steady state condition in order to gain confidence in the CFD and FEA methodologies. Results of the full-load steady state thermal-fluid analysis were found in agreement with the temperature values measured at specific locations on the outer surfaces of the inlet links and outlet manifold. It was found from the subsequent structural analysis that peak effective stresses were located at the connecting regions of the components and were well below the allowed stress values. Higher temperature differences were observed between the thick-walled HPSH1 components during the cold start-up transient as compared to the full-load steady state operating condition. This was because of the rapid temperature changes that occurred, especially in the steam temperature at the HPSH1 entry, and the different rates of heating or cooling for components with different wall thicknesses. Results of the transient thermal-fluid analysis will be used in future to perform structural analysis of the HPSH1. The developed CFD and FEA models are capable of analyzing various other transients (e.g., hot start-up and shut-down) and determine their influence on the durability of plant components.
ContributorsHardeep Singh (Author) / Roy, Ramendra P. (Thesis advisor) / Lee, Taewoo (Thesis advisor) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation presents methods for addressing research problems that currently can only adequately be solved using Quality Reliability Engineering (QRE) approaches especially accelerated life testing (ALT) of electronic printed wiring boards with applications to avionics circuit boards. The methods presented in this research are generally applicable to circuit boards, but the data generated and their analysis is for high performance avionics. Avionics equipment typically requires 20 years expected life by aircraft equipment manufacturers and therefore ALT is the only practical way of performing life test estimates. Both thermal and vibration ALT induced failure are performed and analyzed to resolve industry questions relating to the introduction of lead-free solder product and processes into high reliability avionics. In chapter 2, thermal ALT using an industry standard failure machine implementing Interconnect Stress Test (IST) that simulates circuit board life data is compared to real production failure data by likelihood ratio tests to arrive at a mechanical theory. This mechanical theory results in a statistically equivalent energy bound such that failure distributions below a specific energy level are considered to be from the same distribution thus allowing testers to quantify parameter setting in IST prior to life testing. In chapter 3, vibration ALT comparing tin-lead and lead-free circuit board solder designs involves the use of the likelihood ratio (LR) test to assess both complete failure data and S-N curves to present methods for analyzing data. Failure data is analyzed using Regression and two-way analysis of variance (ANOVA) and reconciled with the LR test results that indicating that a costly aging pre-process may be eliminated in certain cases. In chapter 4, vibration ALT for side-by-side tin-lead and lead-free solder black box designs are life tested. Commercial models from strain data do not exist at the low levels associated with life testing and need to be developed because testing performed and presented here indicate that both tin-lead and lead-free solders are similar. In addition, earlier failures due to vibration like connector failure modes will occur before solder interconnect failures.
ContributorsJuarez, Joseph Moses (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie M. (Thesis advisor) / Gel, Esma (Committee member) / Mignolet, Marc (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2012
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
The stability of nanocrystalline microstructural features allows structural materials to be synthesized and tested in ways that have heretofore been pursued only on a limited basis, especially under dynamic loading combined with temperature effects. Thus, a recently developed, stable nanocrystalline alloy is analyzed here for quasi-static (<100 s-1) and dynamic loading (103 to 104 s-1) under uniaxial compression and tension at multiple temperatures ranging from 298-1073 K. After mechanical tests, microstructures are analyzed and possible deformation mechanisms are proposed. Following this, strain and strain rate history effects on mechanical behavior are analyzed using a combination of quasi-static and dynamic strain rate Bauschinger testing. The stable nanocrystalline material is found to exhibit limited flow stress increase with increasing strain rate as compared to that of both pure, coarse grained and nanocrystalline Cu. Further, the material microstructural features, which includes Ta nano-dispersions, is seen to pin dislocation at quasi-static strain rates, but the deformation becomes dominated by twin nucleation at high strain rates. These twins are pinned from further growth past nucleation by the Ta nano-dispersions. Testing of thermal and load history effects on the mechanical behavior reveals that when thermal energy is increased beyond 200 °C, an upturn in flow stress is present at strain rates below 104 s-1. However, in this study, this simple assumption, established 50-years ago, is shown to break-down when the average grain size and microstructural length-scale is decreased and stabilized below 100nm. This divergent strain-rate behavior is attributed to a unique microstructure that alters slip-processes and their interactions with phonons; thus enabling materials response with a constant flow-stress even at extreme conditions. Hence, the present study provides a pathway for designing and synthesizing a new-level of tough and high-energy absorbing materials.
ContributorsTurnage, Scott Andrew (Author) / Solanki, Kiran N (Thesis advisor) / Rajagopalan, Jagannathan (Committee member) / Peralta, Pedro (Committee member) / Darling, Kristopher A (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2017
Description
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
This paper describes an effort to bring wing structural stiffness and aeroelastic considerations early in the conceptual design process with an automated tool. Stiffness and aeroelasticity can be well represented with a stochastic model during conceptual design because of the high level of uncertainty and variability in wing non-structural mass such as fuel loading and control surfaces. To accomplish this, an improvement is made to existing design tools utilizing rule based automated design to generate wing torque box geometry from a specific wing outer mold-line. Simple analysis on deflection and inferred stiffness shows how early conceptual design choices can strongly impact the stiffness of the structure. The impacts of design choices and how the buckling constraints drive structural weight in particular examples are discussed. The model is then carried further to include a finite element model (FEM) to analyze resulting mode shapes and frequencies for use in aeroelastic analysis. The natural frequencies of several selected wing torque boxes across a range of loading cases are compared.
ContributorsMiskin, Daniel L (Author) / Takahashi, Timothy T (Thesis advisor) / Mignolet, Marc (Committee member) / Murthy, Raghavendra (Committee member) / Arizona State University (Publisher)
Created2018
Description
One type of assistive device for the blind has attempted to convert visual information into information that can be perceived through another sense, such as touch or hearing. A vibrotactile haptic display assistive device consists of an array of vibrating elements placed against the skin, allowing the blind individual to receive visual information through touch. However, these approaches have two significant technical challenges: large vibration element size and the number of microcontroller pins required for vibration control, both causing excessively low resolution of the device. Here, I propose and investigate a type of high-resolution vibrotactile haptic display which overcomes these challenges by utilizing a ‘microbeam’ as the vibrating element. These microbeams can then be actuated using only one microcontroller pin connected to a speaker or surface transducer. This approach could solve the low-resolution problem currently present in all haptic displays. In this paper, the results of an investigation into the manufacturability of such a device, simulation of the vibrational characteristics, and prototyping and experimental validation of the device concept are presented. The possible reasons of the frequency shift between the result of the forced or free response of beams and the frequency calculated based on a lumped mass approximation are investigated. It is found that one of the important reasons for the frequency shift is the size effect, the dependency of the elastic modulus on the size and kind of material. This size effect on A2 tool steel for Micro-Meso scale cantilever beams for the proposed system is investigated.
ContributorsWi, Daehan (Author) / SODEMANN, ANGELA A (Thesis advisor) / Redkar, Sangram (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2019